Systems and methods for image reconstruction

ABSTRACT

Systems and methods for image reconstruction are provided. The methods may include obtaining a first image sequence of a subject and obtaining an initial input function that relates to a concentration of an agent in blood vessels of the subject with respect to time. The first image sequence may include one or more first images generated based on a first portion of scan data of the subject. The methods may further include, for each of a plurality of pixels in the one or more first images, determining at least one correction parameter associated with the pixel and determining, based on the initial input function and the at least one correction parameter, a target input function The methods may further include generating one or more target image sequences related to one or more dynamic parameters based at least in part on a plurality of target input functions.

TECHNICAL FIELD

The present disclosure generally relates to image processing, and moreparticularly, relates to systems and methods for reconstructingparametric images.

BACKGROUND

PET technology has been widely used for clinical examination and medicaldiagnosis. A radioactive tracer is usually administered to a patientbefore a PET scan is performed on a region of interest (ROI) of thepatient. The PET system can detect radiation emitted by the radioactivetracer and reconstruct parametric images corresponding to one or moredynamic parameters associated with biological activities in the ROI. Theparametric images may be used for the evaluation of the physiology(functionality) and/or anatomy (structure) of an organ and/or tissue inthe ROI. For determining the one or more dynamic parameters, the sameinput function that indicates the concentration of the radioactivetracer in the blood or plasma is often applied to various parts of thebody. A single input function does not consider effects of factors suchas inhomogeneous velocity of the blood in different blood vessels, and adistance between a blood sampling site and a specific organ or tissue inthe ROI. Therefore, it is desired to develop systems and methods fordetermining more accurate input functions, and thus generating moreaccurate parametric images based on the actual input functions.

SUMMARY

According to an aspect of the present disclosure, a system for imagereconstruction is provided. The system may include at least onenon-transitory storage medium including a set of instructions and atleast one processor in communication with the at least onenon-transitory storage medium. When executing the set of instructions,the at least one processor may be configured to cause the system toperform operations including obtaining a first image sequence of asubject and obtaining an initial input function that relates to aconcentration of an agent in blood vessels of the subject with respectto time. The first image sequence may include one or more first imagesgenerated based on a first portion of scan data of a scan of thesubject. The agent may be administered to the subject before the scan.For each of a plurality of pixels in the one or more first images, theat least one processor may be configured to cause the system to performoperations including determining at least one correction parameterassociated with the pixel and determining, based on the initial inputfunction and the at least one correction parameter, a target inputfunction associated with the pixel. The at least one processor may befurther configured to cause the system to perform operations includinggenerating one or more target image sequences based at least in part ona plurality of target input functions associated with the plurality ofpixels in the one or more first images. The one or more target imagesequences may relate to one or more dynamic parameters associated withthe subject, respectively.

In some embodiments, the at least one correction parameter may includeat least one of a first parameter associated with a dispersion effectcaused by blood circulation, or a second parameter associated with atime delay effect caused by blood circulation.

In some embodiments, the agent may include a radioactive tracer.

In some embodiments, the one or more dynamic parameters include a set offirst dynamic parameters, and to determine the at least one correctionparameter associated with the pixel, the at least one processor may beconfigured to cause the system to perform operations includingdetermining, based on the one or more first images in the first imagesequence, an output function that indicates a concentration of the agentin at least a part of a tissue of the subject. The at least oneprocessor may be further configured to cause the system to performoperations including determining, using a first kinetic model, arelationship among the set of first dynamic parameters associated withthe subject, the initial input function, the at least one correctionparameter, and the output function. The at least one processor may befurther configured to cause the system to perform operations includingdetermining the at least one correction parameter based on therelationship, the initial input function, and the output function.

In some embodiments, the first kinetic model may be a one-tissuecompartment model.

In some embodiments, to determine the at least one correction parameterassociated with the initial input function, the at least one processormay be further configured to cause the system to perform operationsincluding determining the at least one correction parameter basedfurther on a preset condition associated with one or more physiologicalproperties of the subject.

In some embodiments, to generate the one or more target image sequences,the at least one processor may be further configured to cause the systemto perform operations including generating, based on the relationshipamong the set of first dynamic parameters, at least one of the one ormore target image sequences corresponding to at least one of the set offirst dynamic parameters.

In some embodiments, the one or more dynamic parameters may include aset of second dynamic parameters, and to generate the one or more targetimage sequences, the at least one processor may be configured to causethe system to perform operations including obtaining a second imagesequence generated based on a second portion of the scan data andgenerating at least one of the one or more target image sequencescorresponding to at least one second dynamic parameter based on a secondkinetic model, the second image sequence, and the plurality of targetinput functions associated with the plurality of pixels in the one ormore first images The second image sequence may include one or moresecond images.

In some embodiments, the second portion of the scan data may at leastpartially overlap the first portion of the scan data.

In some embodiments, the second portion of the scan data may include thefirst portion of the scan data.

In some embodiments, to generate the one or more target image sequences,the at least one processor may be configured to cause the system toperform operations including generating at least one of the one or moretarget image sequences corresponding to at least one second dynamicparameter by performing an iterative operation based on a projectionmodel, a second kinetic model, and the scan data.

In some embodiments, the iterative operation may include a maximumlikelihood estimation operation.

According to another aspect of the present disclosure, a method forimage reconstruction is provided. The method may be implemented on acomputing device having at least one processor and at least onenon-transitory storage medium. The method may include obtaining a firstimage sequence of a subject and obtaining an initial input function thatrelates to a concentration of an agent in blood vessels of the subjectwith respect to time. The first image sequence may include one or morefirst images generated based on a first portion of scan data of a scanof the subject. The agent may be administered to the subject before thescan. The method may further include, for each of a plurality of pixelsin the one or more first images, determining at least one correctionparameter associated with the pixel and determining, based on theinitial input function and the at least one correction parameter, atarget input function associated with the pixel. The method may furtherinclude generating one or more target image sequences based at least inpart on a plurality of target input functions associated with theplurality of pixels in the one or more first images. The one or moretarget image sequences may relate to one or more dynamic parametersassociated with the subject, respectively.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities, andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. The drawings are not to scale. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary imaging systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary computing device according to someembodiments of the present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device according to someembodiments of the present disclosure;

FIG. 4 is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process for generatingat least one target image sequence according to some embodiments of thepresent disclosure;

FIG. 6 is a flowchart illustrating an exemplary process for determiningat least one correction parameter for a pixel according to someembodiments of the present disclosure;

FIG. 7 is a flowchart illustrating an exemplary process for generatingat least one target image sequence corresponding to at least one seconddynamic parameter using an indirect reconstruction algorithm accordingto some embodiments of the present disclosure; and

FIG. 8 is a flowchart illustrating an exemplary process for generatingat least one target image sequence corresponding to at least one seconddynamic parameter using an indirect reconstruction algorithm accordingto some embodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well-known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, sections or assembly of differentlevels in ascending order. However, the terms may be displaced byanother expression if they achieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or another storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices (e.g., processor 210 as illustrated in FIG. 2) may beprovided on a computer-readable medium, such as a compact disc, adigital video disc, a flash drive, a magnetic disc, or any othertangible medium, or as a digital download (and can be originally storedin a compressed or installable format that needs installation,decompression, or decryption prior to execution). Such software code maybe stored, partially or fully, on a storage device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an EPROM. It will befurther appreciated that hardware modules/units/blocks may be includedin connected logic components, such as gates and flip-flops, and/or canbe included of programmable units, such as programmable gate arrays orprocessors. The modules/units/blocks or computing device functionalitydescribed herein may be implemented as software modules/units/blocks butmay be represented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description may beapplicable to a system, an engine, or a portion thereof.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

Provided herein are systems and components for an imaging system. Insome embodiments, the imaging system may include a single modalityimaging system and/or a multi-modality imaging system. The singlemodality imaging system may include, for example, a PET system, a SPECTsystem, or the like, or any combination thereof. The multi-modalityimaging system may include, for example, a positron emissiontomography-X-ray imaging (PET-X-ray) system, a single photon emissioncomputed tomography-magnetic resonance imaging (SPECT-MRI) system, apositron emission tomography-computed tomography (PET-CT) system, adigital subtraction angiography-magnetic resonance imaging (DSA-MRI)system, etc. It should be noted that the imaging system described belowis merely provided for illustration purposes, and not intended to limitthe scope of the present disclosure.

The present disclosure provides mechanisms (which can include methods,systems, computer-readable mediums, etc.) for reconstructing parametricimages. As used herein, a parametric image, also referred to as a targetimage sequence, corresponds to a dynamic parameter associated with anorgan, a tissue, or a part thereof. A first image sequence may bedetermined based on at least a portion of scan data (e.g., scan data ofan early stage of a full scan) of the subject. To more accuratelydetermine an input function that relates to a concentration of an agentin blood vessels (e.g., in the blood or the plasma) of a subject, atleast one correction parameter may be determined for each of a pluralityof pixels (or voxels) in one or more first images (e.g., standardizeduptake value (SUV) images) in the first image sequence. For example, theat least one correction parameter may include a first parameterassociated with a dispersion effect and/or a second parameter associatedwith a time delay effect. For instance, a relationship among a set offirst dynamic parameters associated with the subject, an initial inputfunction, the at least one correction parameter, and an output functionmay be determined using a first kinetic model (e.g., a one-tissuecompartment model). The output function may indicate a concentration ofthe agent in at least a part of a tissue of the subject. The at leastone correction parameter may be estimated based on the relationship, theinitial input function, and the output function using an iterativealgorithm, such as a Maximum Likelihood Estimation (MLE) algorithm. Inthis manner, a corrected input function (also referred to as a targetinput function) may be determined for each of the plurality of pixels.One or more parametric images may be more accurately generated based atleast in part on the plurality of corrected input functions associatedwith the plurality of pixels. The correction of input functions on thepixel level may also obviate the need to identify various organs ortissues in an image (sometimes performed manually) for the purposes ofperforming an organ-based or tissue-based correction of input functions,which in turn avoids the need to acquire an image of at least certainaccuracy and perform image segmentation on the image. Accordingly, thesystems and methods disclosed herein may improve accuracy of the inputfunctions and the resulting parametric images and achieve automation ofthe processes of input function correction and/or image reconstruction.

FIG. 1 is a schematic diagram illustrating an exemplary imaging system100 according to some embodiments of the present disclosure. As shown,the imaging system 100 may include a scanner 110, a network 120, one ormore terminals 130, a processing device 140, and a storage device 150.In some embodiments, the scanner 110, the terminal(s) 130, theprocessing device 140, and/or the storage device 150 may be connected toand/or communicate with each other via a wireless connection (e.g., thenetwork 120), a wired connection, or a combination thereof. Theconnection between the components of the imaging system 100 may bevariable. Merely by way of example, the scanner 110 may be connected tothe processing device 140 through the network 120, as illustrated inFIG. 1. As another example, the scanner 110 may be connected to theprocessing device 140 directly. As a further example, the storage device150 may be connected to the processing device 140 through the network120, as illustrated in FIG. 1, or connected to the processing device 140directly. As still a further example, a terminal 130 may be connected tothe processing device 140 through the network 120, as illustrated inFIG. 1, or connected to the processing device 140 directly.

The scanner 110 may generate or provide image data via scanning asubject (e.g., a patient) disposed on a scanning table of the scanner110. In some embodiments, the scanner 110 may be a Positron EmissionTomography (PET) device, a Single Photon Emission Computed Tomography(SPECT) device, a Positron Emission Tomography-Computed Tomography(PET-CT) device, a Single Photon Emission Computed Tomography-MagneticResonance Imaging (SPECT-MRI) system, etc. In some embodiments, thesubject may include a body, a substance, an object, or the like, or acombination thereof. In some embodiments, the subject may include aspecific portion of a body, such as a head, a thorax, an abdomen, or thelike, or a combination thereof. In some embodiments, the subject mayinclude a specific organ or region of interest, such as an esophagus, atrachea, a bronchus, a stomach, a gallbladder, a small intestine, acolon, a bladder, a ureter, a uterus, a fallopian tube, etc.

In some embodiments, the scanner 110 may include The scanner 110 mayinclude a gantry, a detector, an electronics module, a table, and/orother components not shown, for example, a cooling assembly. The scanner110 may scan a subject and obtain information related to the subject.The gantry may support components (e.g., the detector) necessary toproduce and detect radiation events to generate an image. The table mayposition a subject in a detection region. The detector may detectradiation events (e.g., gamma photons) emitted from the detectionregion. In some embodiments, the detector may include a plurality ofdetector units. The detector units may be implemented in a suitablemanner, for example, a ring, a rectangle, or an array. In someembodiments, the detector unit may include one or more crystal elementsand/or one or more photomultiplier tubes (PMT) (not shown). In someembodiments, a PMT as employed in the present disclosure may be asingle-channel PMT or a multi-channel PMT. The electronics module maycollect and/or process electrical signals (e.g., scintillation pulses)generated by the detector. The electronics module may include an adder,a multiplier, a subtracter, an amplifier, a drive circuit, adifferential circuit, a integral circuit, a counter, a filter, ananalog-to-digital converter (ADC), a lower limit detection (LLD)circuit, a constant fraction discriminator (CFD) circuit, atime-todigital converter (TDC), a coincidence circuit, or the like, orany combination thereof. In some embodiments, the detected radiationevents may be stored or archived in a storage (e.g., the storage device150), displayed on a display (e.g., a screen on a computing device), ortransferred to a connected device (e.g., an external database). In someembodiments, a user may control the scanner 110 via a computing device.

In some embodiments, the scanner 110 may be integrated with one or moreother devices that may facilitate the scanning of the subject, such as,an image-recording device. The image-recording device may be configuredto take various types of images related to the subject. For example, theimage-recording device may be a two-dimensional (2D) camera that takespictures of the exterior or outline of the subject. As another example,the image-recording device may be a 3D scanner (e.g., a laser scanner,an infrared scanner, a 3D CMOS sensor) that records the spatialrepresentation of the subject.

The network 120 may include any suitable network that can facilitate theexchange of information and/or data for the imaging system 100. In someembodiments, one or more components of the imaging system 100 (e.g., thescanner 110, the processing device 140, the storage device 150, theterminal(s) 130) may communicate information and/or data with one ormore other components of the imaging system 100 via the network 120. Forexample, the processing device 140 may obtain image data from thescanner 110 via the network 120. As another example, the processingdevice 140 may obtain user instruction(s) from the terminal(s) 130 viathe network 120. The network 120 may be or include a public network(e.g., the Internet), a private network (e.g., a local area network(LAN)), a wired network, a wireless network (e.g., an 802.11 network, aWi-Fi network), a frame relay network, a virtual private network (VPN),a satellite network, a telephone network, routers, hubs, switches,server computers, and/or any combination thereof. For example, thenetwork 120 may include a cable network, a wireline network, afiber-optic network, a telecommunications network, an intranet, awireless local area network (WLAN), a metropolitan area network (MAN), apublic telephone switched network (PSTN), a Bluetooth™ network, aZigBee™ network, a near field communication (NFC) network, or the like,or any combination thereof. In some embodiments, the network 120 mayinclude one or more network access points. For example, the network 120may include wired and/or wireless network access points such as basestations and/or internet exchange points through which one or morecomponents of the imaging system 100 may be connected to the network 120to exchange data and/or information.

The terminal(s) 130 may be connected to and/or communicate with thescanner 110, the processing device 140, and/or the storage device 150.For example, the terminal(s) 130 may obtain a processed image from theprocessing device 140. As another example, the terminal(s) 130 mayobtain image data acquired via the scanner 110 and transmit the imagedata to the processing device 140 to be processed. In some embodiments,the terminal(s) 130 may include a mobile device 131, a tablet computer132, a laptop computer 133, or the like, or any combination thereof. Forexample, the mobile device 131 may include a mobile phone, a personaldigital assistance (PDA), a gaming device, a navigation device, a pointof sale (POS) device, a laptop, a tablet computer, a desktop, or thelike, or any combination thereof. In some embodiments, the terminal(s)130 may include an input device, an output device, etc. The input devicemay include alphanumeric and other keys that may be input via akeyboard, a touch screen (for example, with haptics or tactilefeedback), a speech input, an eye tracking input, a brain monitoringsystem, or any other comparable input mechanism. The input informationreceived through the input device may be transmitted to the processingdevice 140 via, for example, a bus, for further processing. Other typesof input devices may include a cursor control device, such as a mouse, atrackball, or cursor direction keys, etc. The output device may includea display, a speaker, a printer, or the like, or a combination thereof.In some embodiments, the terminal(s) 130 may be part of the processingdevice 140.

In some embodiments, the terminal(s) 130 may send and/or receiveinformation for parametric image reconstruction to the processing device140 via a user interface. The user interface may be in the form of anapplication for parametric image reconstruction implemented on theterminal(s) 130. The user interface implemented on the terminal(s) 130may be configured to facilitate communication between a user and theprocessing device 130. In some embodiments, a user may input a requestfor parametric image reconstruction via the user interface implementedon the terminal(s) 130. The terminal(s) 130 may send the request forparametric image reconstruction to the processing device 140 forreconstructing a parametric image based on a plurality of target inputfunctions as described elsewhere in the present disclosure (e.g., FIG. 5and the descriptions thereof). In some embodiments, the user may inputand/or adjust parameters (e.g., weights) of the target machine learningmodel via the user interface.

The storage device 150 may store data, instructions, and/or any otherinformation. In some embodiments, the storage device 150 may store dataobtained from the processing device 140, the terminal(s) 130, and/or thescanner 110. For example, the storage device 150 may store scan dataobtained from the scanner 110. As another example, the storage device150 may store one or more reconstructed parametric images (i.e., targetimage sequences). In some embodiments, the storage device 150 may storedata and/or instructions that the processing device 140 may execute oruse to perform exemplary methods described in the present disclosure. Insome embodiments, the storage device 150 may include a mass storage, aremovable storage, a volatile read-and-write memory, a read-only memory(ROM), or the like, or any combination thereof. Exemplary mass storagemay include a magnetic disk, an optical disk, a solid-state drive, etc.Exemplary removable storage may include a flash drive, a floppy disk, anoptical disk, a memory card, a zip disk, a magnetic tape, etc. Exemplaryvolatile read-and-write memory may include a random access memory (RAM).Exemplary RAM may include a dynamic RAM (DRAM), a double date ratesynchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), a thyristorRAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc. Exemplary ROM mayinclude a mask ROM (MROM), a programmable ROM (PROM), an erasableprogrammable ROM (EPROM), an electrically erasable programmable ROM(EEPROM), a compact disk ROM (CD-ROM), and a digital versatile disk ROM,etc. In some embodiments, the storage device 150 may be implemented on acloud platform as described elsewhere in the disclosure.

In some embodiments, the storage device 150 may be connected to thenetwork 120 to communicate with one or more other components of theimaging system 100 (e.g., the processing device 140, the terminal(s)130). One or more components of the imaging system 100 may access thedata or instructions stored in the storage device 150 via the network120. In some embodiments, the storage device 150 may be part of theprocessing device 140.

In some embodiments, a three-dimensional coordinate system may be usedin the imaging system 100 as illustrated in FIG. 1. A first axis may beparallel to the lateral direction of the scanning table 114 (e.g., the Xdirection perpendicular to and pointing out of the paper as shown inFIG. 1). A second axis may be parallel to the longitudinal direction ofthe scanning table 114 (e.g., the Z direction as shown in FIG. 1). Athird axis may be along a vertical direction of the scanning table 114(e.g., the Y direction as shown in FIG. 1). The origin of thethree-dimensional coordinate system may be any point in the space. Theorigin of the three-dimensional coordinate system may be determined byan operator. The origin of the three-dimensional coordinate system maybe determined by the imaging system 100.

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. For example, thestorage device 150 may be a data storage including cloud computingplatforms, such as public cloud, private cloud, community, and hybridclouds, etc. However, those variations and modifications do not departthe scope of the present disclosure.

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary computing device 200 on which theprocessing device 140 may be implemented according to some embodimentsof the present disclosure. As illustrated in FIG. 2, the computingdevice 200 may include a processor 210, a storage 220, an input/output(I/O) 230, and a communication port 240.

The processor 210 may execute computer instructions (e.g., program code)and perform functions of the processing device 140 in accordance withtechniques described herein. The computer instructions may include, forexample, routines, programs, objects, components, data structures,procedures, modules, and functions, which perform particular functionsdescribed herein. For example, the processor 210 may process image dataobtained from the scanner 110, the terminals 130, the storage device150, and/or any other component of the imaging system 100. In someembodiments, the processor 210 may include one or more hardwareprocessors, such as a microcontroller, a microprocessor, a reducedinstruction set computer (RISC), an application specific integratedcircuits (ASICs), an application-specific instruction-set processor(ASIP), a central processing unit (CPU), a graphics processing unit(GPU), a physics processing unit (PPU), a microcontroller unit, adigital signal processor (DSP), a field programmable gate array (FPGA),an advanced RISC machine (ARM), a programmable logic device (PLD), anycircuit or processor capable of executing one or more functions, or thelike, or any combinations thereof.

Merely for illustration, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors, thus operations and/or method operations that are performedby one processor as described in the present disclosure may also bejointly or separately performed by the multiple processors. For example,if in the present disclosure the processor of the computing device 200executes both operation A and operation B, it should be understood thatoperation A and operation B may also be performed by two or moredifferent processors jointly or separately in the computing device 200(e.g., a first processor executes operation A and a second processorexecutes operation B, or the first and second processors jointly executeoperation s A and B).

The storage 220 may store data/information obtained from the scanner110, the terminals 130, the storage device 150, and/or any othercomponent of the imaging system 100. In some embodiments, the storage220 may include a mass storage, a removable storage, a volatileread-and-write memory, a read-only memory (ROM), or the like, or anycombination thereof. For example, the mass storage may include amagnetic disk, an optical disk, a solid-state drives, etc. The removablestorage may include a flash drive, a floppy disk, an optical disk, amemory card, a zip disk, a magnetic tape, etc. The volatileread-and-write memory may include a random access memory (RAM). The RAMmay include a dynamic RAM (DRAM), a double date rate synchronous dynamicRAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. The ROM may include a mask ROM (MROM),a programmable ROM (PROM), an erasable programmable ROM (EPROM), anelectrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage 220 may store one or more programs and/or instructions toperform exemplary methods described in the present disclosure. Forexample, the storage 220 may store a program for the processing device140 for determining the position of a target region of a subject (e.g.,a target portion of a patient).

The I/O 230 may input and/or output signals, data, information, etc. Insome embodiments, the I/O 230 may enable user interaction with theprocessing device 140. In some embodiments, the I/O 230 may include aninput device and an output device. Examples of the input device mayinclude a keyboard, a mouse, a touch screen, a microphone, or the like,or a combination thereof. Examples of the output device may include adisplay device, a loudspeaker, a printer, a projector, or the like, or acombination thereof. Examples of the display device may include a liquidcrystal display (LCD), a light-emitting diode (LED)-based display, aflat panel display, a curved screen, a television device, a cathode raytube (CRT), a touch screen, or the like, or a combination thereof.

The communication port 240 may be connected to a network (e.g., thenetwork 120) to facilitate data communications. The communication port240 may establish connections between the processing device 140 and thescanner 110, the terminals 130, and/or the storage device 150. Theconnection may be a wired connection, a wireless connection, any othercommunication connection that can enable data transmission and/orreception, and/or any combination of these connections. The wiredconnection may include, for example, an electrical cable, an opticalcable, a telephone wire, or the like, or any combination thereof. Thewireless connection may include, for example, a Bluetooth™ link, aWi-Fi™ link, a WiMax™ link, a WLAN link, a ZigBee™ link, a mobilenetwork link (e.g., 3G, 4G, 5G), or the like, or a combination thereof.In some embodiments, the communication port 240 may be and/or include astandardized communication port, such as RS232, RS485, etc. In someembodiments, the communication port 240 may be a specially designedcommunication port. For example, the communication port 240 may bedesigned in accordance with the digital imaging and communications inmedicine (DICOM) protocol.

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device 300 on which theterminals 130 may be implemented according to some embodiments of thepresent disclosure. As illustrated in FIG. 3, the mobile device 300 mayinclude a communication platform 310, a display 320, a graphicprocessing unit (GPU) 330, a central processing unit (CPU) 340, an I/O350, a memory 360, and a storage 390. In some embodiments, any othersuitable component, including but not limited to a system bus or acontroller (not shown), may also be included in the mobile device 300.In some embodiments, a mobile operating system 370 (e.g., iOS™,Android™, Windows Phone™) and one or more applications 380 may be loadedinto the memory 360 from the storage 390 in order to be executed by theCPU 340. The applications 380 may include a browser or any othersuitable mobile apps for receiving and rendering information relating toimage processing or other information from the processing device 140.User interactions with the information stream may be achieved via theI/O 350 and provided to the processing device 140 and/or othercomponents of the imaging system 100 via the network 120.

To implement various modules, units, and functionalities described inthe present disclosure, computer hardware platforms may be used as thehardware platform(s) for one or more of the elements described herein. Acomputer with user interface elements may be used to implement apersonal computer (PC) or any other type of workstation or terminaldevice. A computer may also act as a server if appropriately programmed.

FIG. 4 is a block diagram illustrating an exemplary processing device140 according to some embodiments of the present disclosure. Asillustrated in FIG. 4, the processing device 140 may include anobtaining module 410, a correction parameter determination module 420, atarget input function determination module 430, and a target imagesequence generation module 440. The modules may be hardware circuits ofall or part of the processing device 140. The modules may also beimplemented as an application or a set of instructions read and executedby the processing device 140. Further, the modules may be anycombination of the hardware circuits and the application/instructions.For example, the modules may be part of the processing device 140 whenthe processing device 140 is executing the application/set ofinstructions.

The obtaining module 410 may obtain data related to image processing. Insome embodiments, the obtaining module 410 may obtain a first imagesequence of a subject. An agent may be administered to the subjectbefore a scan (e.g., a PET scan, a SPECT scan) is performed on a regionof interest (ROI) of the subject. For example, the agent may be aradioactive tracer. In some embodiments, the radioactive tracer mayinclude a substrate of metabolism at the ROI of the subject (e.g., ananimal or a patient). The distribution of the radioactive tracer mayindicate information of biological activities in the ROI. In someembodiments, the one or more first images may be generated based on afirst portion of the scan data corresponding to an early stage of thescan (e.g., within the first 90 seconds, 150 seconds, or 180 seconds ofthe scan). In some embodiments, the obtaining module 410 may obtain aninitial input function that relates to a concentration of the agent inblood vessels of the subject with respect to time. The initial inputfunction may be a blood TAC that indicates the TAC of the agent in thewhole blood, or a plasma TAC that indicates the TAC of the agent in theplasma. In some embodiments, the obtaining module 410 may obtain asecond image sequence that is generated based on a second portion of thescan data. The second image sequence may include one or more secondimages. In some embodiments, the second portion of the scan data maycorrespond to the rest of the scan after the early stage. In someembodiments, the second portion of the scan data may at least partiallyoverlap the first portion of the scan data. For instance, the secondportion of the scan data may correspond to the whole scan.

The correction parameter determination module 420 may determine at leastone correction parameter for each of the plurality of pixels in thefirst image sequence. In some embodiments, the input function associatedwith the agent at various positions of the body of the subject may vary,for example, due to a dispersion effect, a time delay effect, etc. Theat least one correction parameter may include a first parameterassociated with the dispersion effect and/or a second parameterassociated with the time delay effect. The correction parameterdetermination module 420 may determine, based on the one or more firstimages in the first image sequence, an output function that relates to aconcentration of the agent in at least a part of a tissue of thesubject. The correction parameter determination module 420 may furtherdetermine, using a first kinetic model, a relationship among a set offirst dynamic parameters associated with the subject, the initial inputfunction, the at least one correction parameter, and the outputfunction. The set of first dynamic parameters may be related to thefirst kinetic model. The first kinetic model may describe kineticsrelating to the agent after the agent is administered to the subject.The first kinetic model may be a relatively simple kinetic model, suchas a one-tissue compartment model. For instance, the set of firstdynamic parameters may include a transportation rate of the agent fromplasma to tissue, a transport rate of the agent from the tissue to theplasma, a concentration of plasma in the tissue, or the like, or anycombination thereof. The correction parameter determination module 420may further determine the at least one correction parameter based on therelationship, the initial input function, and the output function.

The target input function determination module 430 may determine atarget input function for each of the plurality of pixels in the firstimage sequence. Since the target input function is determined for eachof the plurality of pixels in the one or more first images, the targetinput function may be more accurate than the single initial inputfunction applied to each of the plurality of pixels in the one or morefirst images.

The target image sequence generation module 440 may generate one or moretarget image sequences corresponding to one or more dynamic parameters.In some embodiments, the target image sequence generation module 440 maygenerate at least one target image sequence corresponding to at leastone of the set of first dynamic parameters (e.g., the transportationrate of the agent from plasma to tissue, the concentration of plasma inthe tissue) based on the relationship among the set of first dynamicparameters, the initial input function, the at least one correctionparameter, and the output function. In some embodiments, the firstparameter and/or the second parameter may also be considered as dynamicparameter(s). The target image sequence generation module 440 maygenerate the target image sequence(s) corresponding to the firstparameter and/or the second parameter. In some embodiments, the targetimage sequence generation module 440 may further generate at least onetarget image sequence corresponding to at least one second dynamicparameter. In some embodiments, at least one of the one or more seconddynamic parameters may be different from the set of first dynamicparameters. For example, the second dynamic parameters may includedynamic parameters associated with a second kinetic model. Merely by wayof example, the second kinetic model may be a two-tissue compartmentmodel.

The target image sequence generation module 440 may reconstruct the atleast one target image sequence corresponding to the at least one seconddynamic parameter using an indirect reconstruction algorithm or a directreconstruction algorithm. Using the indirect reconstruction algorithm,the target image sequence generation module 440 may obtain a secondimage sequence generated based on a second portion of the scan data. Thesecond image sequence may include one or more second images that presentthe uptake of the agent in the ROI. In some embodiments, the secondportion of the scan data may correspond to the rest of the scan afterthe early stage. In some embodiments, the second portion of the scandata may at least partially overlap the first portion of the scan data.Then the target image sequence generation module 440 may generate atleast one target image sequence corresponding to at least one seconddynamic parameter based on a second kinetic model, the second imagesequence, and the plurality of target input functions associated withthe plurality of pixels in the one or more first images. Using thedirect reconstruction algorithm, the target image sequence generationmodule 440 may determine an estimation function based on a secondkinetic model and a projection model. The target image sequencegeneration module 440 may further determine the at least one seconddynamic parameter based on the estimation function and the scan data.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. In someembodiments, any module mentioned above may be divided into two or moreunits. In some embodiments, the processing device 140 may include one ormore additional modules. For example, the processing device 140 mayfurther include a control module configured to generate control signalsfor one or more components in the imaging system 100.

FIG. 5 is a flowchart illustrating an exemplary process for generatingat least one target image sequence according to some embodiments of thepresent disclosure. At least a portion of process 500 may be implementedon the computing device 200 as illustrated in FIG. 2 or the mobiledevice 300 as illustrated in FIG. 3. In some embodiments, one or moreoperations of the process 500 may be implemented in the imaging system100 as illustrated in FIG. 1. In some embodiments, one or moreoperations in the process 500 may be stored in the storage device 150and/or the storage 220 (e.g., a ROM, a RAM, etc.) as the form ofinstructions, and invoked and/or executed by the processing device 140,or the processor 210 of the processing device 140. In some embodiments,the instructions may be transmitted to one or more components of thesystem 100 in the form of electronic current or electrical signals.

In 502, the processing device 140 (e.g., the obtaining module 410) mayobtain a first image sequence of a subject. An agent may be administeredto the subject before a scan (e.g., a PET scan, a SPECT scan) isperformed on a region of interest (ROI) of the subject. For example, theagent may be a radioactive tracer. In some embodiments, the radioactivetracer may include a substrate of metabolism at the ROI of the subject(e.g., an animal or a patient). The distribution of the radioactivetracer may indicate information of biological activities in the ROI. Forinstance, the radioactive tracer may include [¹⁵O]H₂O, [¹⁵O]butanol,[¹¹C]butanol [¹⁸F]fluorodeoxyglucose (FDG), [⁶⁴Cu]diacetyl-bis(⁶⁴Cu-ATSM), [¹⁸F]fluoride, 3′-deoxy-3′-[¹⁸F]fluorothymidine (FLT),[¹⁸F]-fluoromisonidazole (FMISO), gallium, thallium, or the like, or thelike, or any combination thereof. In some embodiments, the agent may bea therapeutic agent marked with a radioactive isotope, such as ¹⁵O, ¹¹C,etc. The distribution and therapeutic effect of the therapeutic agentmay be estimated based on scan data acquired during the scan.

In some embodiments, the ROI to be scanned may include tissue and/or oneor more organs of the subject. For example, the ROI may be a portion ofthe subject, including the heart, a lung, the liver, the spleen, or thelike, or any combination thereof. As another example, the ROI may be thewhole body of the subject. The first image sequence may include one ormore first images generated based on at least a portion of scan data ofthe scan of the subject. The one or more first images may present theuptake of the tracer in the ROI. For instance, the one or more firstimages may include one or more standardized uptake value (SUV) images.In some embodiments, the one or more first images may be generated basedon a first portion of the scan data corresponding to an early stage ofthe scan (e.g., within the first 90 seconds, 150 seconds, or 180 secondsof the scan). Exemplary image reconstruction algorithms for generatingthe one or more first images may include an iterative algorithm, ananalysis algorithm, etc. The iterative algorithm may include a MaximumLikelihood Estimation (MLE) algorithm, an ordered subset expectationmaximization (OSEM), a 3D reconstruction algorithm, etc. The analysisalgorithm may include a filtered back projection (FBP) algorithm. Insome embodiments, the processing device 140 may obtain the first imagesequence from a storage device (e.g., the storage device 150).

In 504, the processing device 140 (e.g., the obtaining module 410) mayobtain an initial input function that relates to a concentration of theagent in blood vessels of the subject with respect to time. In someembodiments, operation 502 and operation 504 may be performedconcurrently or sequentially in any order. In some embodiments, theinitial input function may be a time activity curve (TAC) associatedwith the agent. The initial input function may be a blood TAC thatindicates the TAC of the agent in the whole blood, or a plasma TAC thatindicates the TAC of the agent in the plasma. In some embodiments, theprocessing device 140 may designate the plasma TAC or the blood TAC ofthe agent in a specific organ (e.g., the heart) or tissue as the initialinput function. In some embodiments, the blood TAC may be converted tothe plasma TAC and then the plasma TAC may be used as the initial inputfunction. In some embodiments, the initial input function may be adual-input function of the agent, for example, when the ROI includes theliver. As used herein, a dual-input function of a tracer or other agentsin an organ or tissue refers to an input function that describes aconcentration of the tracer or agent in the organ or tissue that has twoentry points for the tracer or agent to enter.

The plasma TAC may be obtained using a gold standard technique (e.g.,through the extraction and measurement of arterial blood samples), anarterialization of venous blood technique, a PET blood pool scantechnique, a standard input function technique, a fitting functiontechnique, or the like, or a combination thereof. Using the goldstandard technique, the arterial blood of the subject may be sampled tomeasure plasma TAC of the subject. Using the arterialization of venousblood technique, the venous blood of the subject may be sampled tomeasure plasma TAC of the subject. Using the PET blood pool scantechnique, the plasma TAC of the subject may be determined based on theimage sequence. For example, the processing device 140 may determine anROI (e.g., a region associated with the heart or arterial blood) fromeach of the one or more images in the image sequence. The processingdevice 140 may identify a blood TAC from the one or more images based onthe determined ROI and designate the blood TAC as the plasma TAC. Theplasma TAC identified from the image sequence may be also referred to asan image-derived input function. Using the standard input functiontechnique, the plasma TAC of the subject may be determined based on aplurality of plasma TACs of multiple persons (e.g., patients) determinedbased on the gold standard technique. Further, the plurality of plasmaTACs of multiple persons may be normalized and averaged to obtain theplasma TAC of the subject. Using the fitting function technique, theplasma TAC of the subject may be determined by fitting the plurality ofplasma TACs of multiple persons. The plasma TAC of the subjectdetermined based on the plurality of plasma TACs of multiple persons maybe also referred to as a population-based input function (or standardarterial input function, SAIF). In some embodiments, the plasma TAC ofthe subject may be determined based on the image sequence and theplurality of plasma TACs of multiple persons. The plasma TAC of thesubject determined based on the image-derived input function and thepopulation-based input function may be also referred to as apopulation-based input function normalized by image (also referred to asPBIFNI). For example, the plasma TAC may be determined by normalizingthe population based input function using the image-derived inputfunction. As a further example, the processing device 140 may averagethe population-based input function and the image-derived input functionto obtain the population-based input function normalized by image.

In 506, the processing device 140 (e.g., the correction parameterdetermination module 420) may determine, for each of a plurality ofpixels in the one or more first images, at least one correctionparameter associated with the pixel. As used herein, pixels in2-dimensional images and voxels in 3-dimensional images are bothreferred to as “pixels.” In some embodiments, the input functionassociated with the agent at various positions of the body of thesubject may vary, for example, due to a dispersion effect, a time delayeffect, etc. The dispersion effect and the time delay effect may becaused by blood circulation. Specifically, the dispersion effect may becaused by factors including, e.g., the inhomogeneous velocity of theblood in different blood vessels. The time delay effect may be caused bya distance between a blood sampling site and a specific organ or tissuein the ROI. In some embodiments, the at least one correction parametermay include a first parameter associated with the dispersion effectand/or a second parameter associated with the time delay effect.

To determine the at least one correction parameter, the processingdevice 140 may determine, based on the one or more first images in thefirst image sequence, an output function that relates to a concentrationof the agent in at least a part of a tissue of the subject. Theprocessing device 140 may further determine, using a first kineticmodel, a relationship among a set of first dynamic parameters associatedwith the subject, the initial input function, the at least onecorrection parameter, and the output function. The set of first dynamicparameters may be related to the first kinetic model. The first kineticmodel may describe kinetics relating to the agent after the agent isadministered to the subject. The first kinetic model may be a relativelysimple kinetic model, such as a one-tissue compartment model. Theone-tissue compartment model mainly describes the transport of the agentbetween the blood/plasma and the tissue. For instance, the set of firstdynamic parameters may include a transportation rate of the agent fromplasma to tissue, a transport rate of the agent from the tissue to theplasma, a concentration of plasma in the tissue, or the like, or anycombination thereof. The processing device 140 may further determine theat least one correction parameter based on the relationship, the initialinput function, and the output function. More details regarding thedetermination of the at least one correction parameter may be foundelsewhere in the present disclosure, for example, in the description inconnection with FIG. 6.

In 508, the processing device 140 (e.g., the target input functiondetermination module 430) may determine, based on the initial inputfunction and the at least one correction parameter of the pixel, atarget input function associated with the pixel. Since the target inputfunction is determined for each of the plurality of pixels in the one ormore first images, the target input function may be more accurate thanthe single initial input function applied to each of the plurality ofpixels in the one or more first images. In some embodiments, the targetinput function for the pixel i may be determined using the followingequation (1):

C _(input)(t)=k _(a) e ^(−k) ^(a) ^(t) ⊗C _(p)(t−t _(d)),  (1)

where t denotes time that has lapsed since the tracer or agentadministration; C_(input)(t) denotes the target input function; C_(p)denotes the initial input function; k_(a) denotes the first parameterassociated with the dispersion effect; t_(d) denotes the secondparameter associated with the time delay effect; ⊗ denotes a convolutionoperation.

In 510, the processing device 140 (e.g., the target image sequencegeneration module 440) may generate one or more target image sequencesbased at least in part on a plurality of target input functionsassociated with the plurality of pixels in the one or more first images.A target image sequence may include one or more target images presentingthe value of a dynamic parameter corresponding to one or more timepoints during the scan. For example, the one or more target images maybe one or more static images corresponding to one or more time points.As another example, the target image sequence may include a dynamictarget image, such as a Graphic Interchange Format (GIF) image thatpresents the change of the dynamic parameter with respect to time. Asused herein, the term “dynamic parameter” refers to a physiologicalparameter associated with the kinetics of the agent after the agent isadministered to the subject. The one or more target image sequences mayaid the evaluation of the physiology (functionality) and/or anatomy(structure) of an organ and/or tissue in the ROI. For instance, the oneor more dynamic parameters may include a transportation rate of theagent from plasma to tissue, a transport rate of the agent from thetissue to the plasma, a concentration of plasma in the tissue, aperfusion rate of the agent, a receptor binding potential of the agent,or the like, or any combination thereof.

In some embodiments, at least one target image sequence corresponding toat least one of the set of first dynamic parameters (e.g., thetransportation rate of the agent from plasma to tissue, theconcentration of plasma in the tissue) may be generated based on therelationship among the set of first dynamic parameters, the initialinput function, the at least one correction parameter, and the outputfunction. In some embodiments, the first parameter and/or the secondparameter may also be considered as dynamic parameter(s). The processingdevice 140 may generate the target image sequence(s) corresponding tothe first parameter and/or the second parameter.

In some embodiments, the processing device 140 may further generate atleast one target image sequence corresponding to at least one seconddynamic parameter. In some embodiments, at least one of the one or moresecond dynamic parameters may be different from the set of first dynamicparameters. For example, the second dynamic parameters may includedynamic parameters associated with a second kinetic model. The secondkinetic model may describe kinetics relating to the agent after theagent is administered to the subject. The second kinetic model may be acompartment model or a non-compartment model, a linear model or anon-linear model, a distributed model or a non-distributed model, or thelike, or any combination thereof. Merely by way of example, the secondkinetic model may be a two-tissue compartment model. The two-tissuecompartment model may describe the transport of the agent between theblood/plasma and the tissue, as well as a phosphorylation process of theagent when the tracer is FDG. The one or more second dynamic parametersfor a pixel may include a transportation rate of the agent from plasmato tissue (e.g., at a part of the subject corresponding to the pixel), atransport rate of the agent from the tissue to the plasma, aconcentration of plasma in the tissue, a phosphorylation rate of theagent, a dephosphorylation rate of the agent, or the like, or anycombination thereof.

The processing device 140 may reconstruct the at least one target imagesequence corresponding to the at least one second dynamic parameterusing an indirect reconstruction algorithm or a direct reconstructionalgorithm. Using the indirect reconstruction algorithm, the processingdevice 140 may obtain a second image sequence generated based on asecond portion of the scan data. The second image sequence may includeone or more second images that present the uptake of the agent in theROI. In some embodiments, the second portion of the scan data maycorrespond to the rest of the scan after the early stage. In someembodiments, the second portion of the scan data may at least partiallyoverlap the first portion of the scan data. Then the processing device140 may generate at least one target image sequence corresponding to atleast one second dynamic parameter based on a second kinetic model, thesecond image sequence, and the plurality of target input functionsassociated with the plurality of pixels in the one or more first images.More details regarding an exemplary indirect reconstruction algorithmmay be found elsewhere in the present disclosure, for example, in thedescription of FIG. 7. Using the direct reconstruction algorithm, theprocessing device 140 may determine an estimation function based on asecond kinetic model and a projection model. The processing device 140may further determine the at least one second dynamic parameter based onthe estimation function and the scan data. More details regarding anexemplary direct reconstruction algorithm may be found elsewhere in thepresent disclosure, for example, in the description of FIG. 8.

It should be noted that the above description regarding the process 500is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations or modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure.

FIG. 6 is a flowchart illustrating an exemplary process for determiningat least one correction parameter for a pixel according to someembodiments of the present disclosure. At least a portion of process 600may be implemented on the computing device 200 as illustrated in FIG. 2or the mobile device 300 as illustrated in FIG. 3. In some embodiments,one or more operations of the process 600 may be implemented in theimaging system 100 as illustrated in FIG. 1. In some embodiments, one ormore operations in the process 600 may be stored in the storage device150 and/or the storage 220 (e.g., a ROM, a RAM, etc.) as the form ofinstructions, and invoked and/or executed by the processing device 140,or the processor 210 of the processing device 140. In some embodiments,the instructions may be transmitted to one or more components of one ormore components of the system 100 in the form of electronic current orelectrical signals.

In 602, the processing device 140 (e.g., the correction parameterdetermination module 420) may determine, based on the one or more firstimages in the first image sequence, an output function that relates to aconcentration of the agent in at least a part of a tissue of thesubject. For example, the agent may be FDG. The output function mayindicate a total concentration of unphosphorylated FDG andphosphorylated FDG in the tissue. In some embodiments, the processingdevice 140 may determine an output function for each of the plurality ofpixels in the one or more first images.

In 604, the processing device 140 (e.g., the correction parameterdetermination module 420) may determine, using a first kinetic model, arelationship among the set of first dynamic parameters associated withthe subject, the initial input function, the at least one correctionparameter, and the output function. In some embodiments, the firstkinetic model may be a relatively simple kinetic model, such as aone-tissue compartment model. The set of first dynamic parameters mayrelate to the first kinetic model. In some embodiments, the processingdevice 140 may independently determine the first parameter associatedwith the dispersion effect and the second parameter associated with thetime delay effect. Merely by way of example, the relationship betweenthe set of first dynamic parameters may be presented using the followingequation (2):

C(t)=(1−v _(b))C ₁(t)+v _(b) C _(i)(t)=K ₁ e ^(−k) ² ^(t) ⊗C _(i)(t)+v_(b) C _(i)(t),  (2)

where C(t) denotes the output function determined based on the one ormore first images; C₁(t) denotes the TAC of the agent in the firstcompartment of the subject (the amount of tracer(s) transported to thetissue from the plasma); C_(i)(t) denotes the TAC of the agent in theblood or the plasma of the subject (i.e., the input function with delayand dispersion effect); K₁ denotes the transportation rate of the agentfrom plasma/blood to tissue; k₂ denotes the transport rate of the agentfrom the tissue to the plasma/blood; and v_(b) denotes the concentrationof plasma in the tissue. In combination with equation (1), C₁(t) may bepresented using the following equation (3):

C ₁(t)=K ₁(e ^(−k) ² ^(t))⊗C _(i)(t)=∫₀ ^(t)∫₀ ^(γ) K ₁ k _(a)(e ^(−k) ²^((t-γ)))(e ^(−k) ^(a) ^((γ-τ)))C _(p)(τ)dτdγ,  (3)

where k_(a) denotes the first parameter; γ and τ are dummy variables forintegration. In combination with equation (3), the relationship may bepresented using the following equation (4):

C(t)=(1−v _(b))K ₁ k _(a)∫₀ ^(t)∫₀ ^(γ) K ₁ k _(a)(e ^(−k) ² ^((t-γ)))(e^(−k) ^(a) ^((y-τ)))C _(p)(τ)dτdγ+v _(b) k _(a) e ^(−k) ^(a) ^(t) ⊗C_(p)(t).   (4)

In some embodiments, equation (4) may be directly used for estimatingk_(a) based on the initial input function and the output function.

In 606, the processing device 140 (e.g., the correction parameterdetermination module 420) may determine, the at least one correctionparameter based on the relationship, the initial input function, and theoutput function. In some embodiments, C₁(t) may be presented in asimpler way using the following equation (5):

$\begin{matrix}{{C_{1}(t)} = {{{K_{1}\left( e^{{- k_{2}}t} \right)} \otimes {C_{i}(t)}} = {{\int_{0}^{t}{\int_{0}^{y}\ {K_{1}k_{a}{\exp \left( {{{- k_{2}}t} + {k_{2}\gamma} - {k_{a}\gamma} + {k_{a}\tau}} \right)}{C_{p}(\tau)}d\; \tau \; d\; \gamma}}} = {{\int_{0}^{t}{K_{1}k_{a}{C_{p}(\tau)}{\exp \left( {{{- k_{2}}t} + {k_{a}\tau}} \right)}{\int_{\tau}^{t}{{\exp \left( {{- \left( {k_{a} - k_{2}} \right)}\gamma} \right)}d\; \gamma \; d\; \tau}}}} = {{{\int_{0}^{t}{\frac{K_{1}k_{a}}{\left( {k_{a} - k_{2}} \right)}{C_{p}(\tau)}{\exp \left( {- {k_{2}\left( {t - \tau} \right)}} \right)}d\; \tau}} - {\int_{0}^{t}{\frac{K_{1}k_{a}}{\left( {k_{a} - k_{2}} \right)}{C_{p}(\tau)}{\exp \left( {- {k_{a}\left( {t - \tau} \right)}} \right)}d\; \tau}}} = {{\frac{K_{1}k_{a}}{\left( {k_{a} - k_{2}} \right)}{e^{{- k_{2}}t} \otimes {C_{p}(\tau)}}} - {\frac{K_{1}k_{a}}{\left( {k_{a} - k_{2}} \right)}{e^{{- k_{a}}t} \otimes {{C_{p}(\tau)}.}}}}}}}}} & (5)\end{matrix}$

In combination with equation (5), the relationship may be presentedusing the following equation (6):

$\begin{matrix}{{{C(t)} = {{{\left( {1 - v_{b}} \right){C_{1}(t)}} + {v_{b}{C_{i}(t)}}} = {{{\frac{\left( {1 - v_{b}} \right)K_{1}k_{a}}{\left( {k_{a} - k_{2}} \right)}{e^{{- k_{2}}t} \otimes {C_{p}(\tau)}}} + {\left( {{- \frac{\left( {1 - v_{b}} \right)K_{1}k_{a}}{\left( {k_{a} - k_{2}} \right)}} + {v_{b}k_{a}}} \right){e^{{- k_{a}}t} \otimes {C_{p}(\tau)}}}} = {{{K_{1}^{\prime}\left( e^{{- k_{2}}t} \right)} \otimes {C_{p}(t)}} + {v_{b}^{\prime}{e^{{- k_{a}}t} \otimes {C_{p}(t)}}}}}}},\mspace{79mu} {where}} & (6) \\{\mspace{79mu} {{\frac{\left( {1 - v_{b}} \right)K_{1}k_{a}}{\left( {k_{a} - k_{2}} \right)} = K_{1}^{\prime}},{and}}} & (7) \\{\mspace{79mu} {{{v_{b}k_{a}} - \frac{\left( {1 - v_{b}} \right)K_{1}k_{a}}{\left( {k_{a} - k_{2}} \right)}} = {v_{b}^{\prime}.}}} & (8)\end{matrix}$

Similarly, the second parameter associated with the time delay effectmay be presented using the following equation (9):

C(t)=K ₁′(e ^(−k) ² ^(t))⊗C _(p)(t−t _(d))+v _(b) ′e ^(−k) ^(a) ^(t) ⊗C_(p)(t−t _(d)).  (9)

In some embodiments, the processing device 140 may determine the firstparameter and the second parameter based on a preset condition. Thepreset condition may be associated with the physiological properties ofthe subject. For instance, the preset condition may be:

$\left\{ {\begin{matrix}{k_{a} > k_{2}} \\{K_{1}^{\prime} > 0}\end{matrix}.} \right.$

The first parameter and the second parameter may be estimated using aniterative algorithm. Exemplary iterative algorithms may include an MLEalgorithm, an OSEM algorithm, a Maximum Posterior Probability (MAP)algorithm, a Weighted Least Square (WLS) algorithm, or the like, or anycombination thereof. Merely by way of example, assuming that theparameters satisfy the Gaussian distribution, the first parameter andthe second parameter may be estimated using the least square algorithmwith the following equation (10):

$\begin{matrix}{\left( {k_{a},t_{d}} \right) = {\underset{K_{1}^{\prime},k_{2},v_{b}^{\prime},k_{a},t_{d}}{argmin}{\int{\left( {{C(t)} - \left( {{K_{1}^{\prime {(e^{{- k_{2}}t})}} \otimes {C_{p}\left( {t - t_{d}} \right)}} + {v_{b}^{\prime}{e^{{- k_{a}}t} \otimes {C_{p}\left( {t - t_{d}} \right)}}}} \right)} \right)^{2}d\; {t.}}}}} & (10)\end{matrix}$

After the dynamic parameters k_(a), t_(d), K₁′, v_(b)′, and k₂ areestimated, K₁ and v_(b) may be determined accordingly. In someembodiments, one or more target image sequences corresponding to atleast one of the dynamic parameters k_(a), t_(d), K₁′, v_(b)′, k₂, K₁and v_(b) may be generated.

It should be noted that the above description regarding the process 600is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations or modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure.

FIG. 7 is a flowchart illustrating an exemplary process for generatingat least one target image sequence corresponding to at least one seconddynamic parameter using an indirect reconstruction algorithm accordingto some embodiments of the present disclosure. At least a portion ofprocess 700 may be implemented on the computing device 200 asillustrated in FIG. 2 or the mobile device 300 as illustrated in FIG. 3.In some embodiments, one or more operations of the process 700 may beimplemented in the imaging system 100 as illustrated in FIG. 1. In someembodiments, one or more operations in the process 700 may be stored inthe storage device 150 and/or the storage 220 (e.g., a ROM, a RAM, etc.)as the form of instructions, and invoked and/or executed by theprocessing device 140, or the processor 210 of the processing device140. In some embodiments, the instructions may be transmitted to one ormore components of one or more components of the system 100 in the formof electronic current or electrical signals.

In 702, the processing device 140 (e.g., the obtaining module 410) mayobtain a second image sequence generated based on a second portion ofthe scan data, the second image sequence including one or more secondimages. In some embodiments, the second portion of the scan data maycorrespond to the rest of the scan after the early stage. In someembodiments, the second portion of the scan data may at least partiallyoverlap the first portion of the scan data. For instance, the secondportion of the scan data may correspond to the whole scan. The secondimage sequence may include one or more second images that present theuptake of the agent in the ROI. The one or more second images may bereconstructed in a similar manner as the one or more first images asdescribed in operation 502.

In 704, the processing device 140 (e.g., the target image sequencegeneration module 440) may generate at least one target image sequencecorresponding to at least one second dynamic parameter based on thesecond kinetic model, the second image sequence, and the plurality oftarget input functions associated with the plurality of pixels in theone or more first images. In some embodiments, the one or more seconddynamic parameters may be different from the set of first dynamicparameters. For example, the second dynamic parameters may includedynamic parameters associated with a second kinetic model, while thefirst dynamic parameters may include dynamic parameters associated witha first kinetic model. The first kinetic model and the second kineticmodel both describe the kinetics relating to the agent after the agentis administered to the subject. In some embodiments, the first kineticmodel may be simpler than the second kinetic model. Merely by way ofexample, the second kinetic model may be a two-tissue compartment modeland the agent may be FDG. For each of the plurality of pixels in the oneor more second images, a relationship between the input function, theoutput function, and one or more second dynamic parameters may bepresented based on the target input function using the followingequation (11):

C(t)=(1−v _(b))(C ₁(t)+C ₂(t))+v _(b) C _(input)(t),  (11)

where C₁(t) denotes the concentration of unphosphorylated FDG in thetissue; C₂(t) denotes the concentration of phosphorylated FDG in thetissue, where

$\begin{matrix}\left\{ {\begin{matrix}{{C_{1}(t)} = {\frac{K_{1}}{\alpha_{2} - \alpha_{1}}{\left( {{\left( {k_{4} - \alpha_{1}} \right)e^{{- \alpha_{1}}t}} + {\left( {\alpha_{2} - k_{4}} \right)e^{{- \alpha_{2}}t}}} \right) \otimes {C_{if}(t)}}}} \\{{C_{2}(t)} = {\frac{K_{1}k_{3}}{\alpha_{2} - \alpha_{1}}{\left( {e^{{- \alpha_{1}}t} - e^{{- \alpha_{2}}t}} \right) \otimes {C_{if}(t)}}}}\end{matrix},{where}} \right. & (12) \\\left\{ {\begin{matrix}{\alpha_{1} = \frac{k_{2} + k_{3} + k_{4} - \sqrt{\left( {k_{2} + k_{3} + k_{4}} \right)^{2} - {4k_{2}k_{4}}}}{2}} \\{\alpha_{2} = \frac{k_{2} + k_{3} + k_{4} + \sqrt{\left( {k_{2} + k_{3} + k_{4}} \right)^{2} - {4k_{2}k_{4}}}}{2}}\end{matrix},} \right. & (13)\end{matrix}$

where k₃ denotes the phosphorylation rate of the agent; k₄ denotes adephosphorylation rate. In some embodiments, assuming that the dynamicparameters satisfy the Gaussian distribution, k₃ and k₄ may bedetermined using an MLE algorithm with the following equation (14):

$\begin{matrix}{\left( {K_{1},k_{2},k_{3},k_{4},v_{b}} \right) = {\underset{{K_{1,}k_{2}},k_{3},k_{4},v_{b}}{argmin}{\int{\left( {{C(t)} - \left( {{\left( {1 - v_{b}} \right)\left( {{C_{1}(t)} + {C_{2}(t)}} \right)} + {v_{b}{C_{input}(t)}}} \right)} \right)^{2}d\; {t.}}}}} & (14)\end{matrix}$

In some embodiments, the values of K₁, k₂, and v_(b) may be determinedas described in operation 606. In some embodiments, the values of K₁,k₂, and v_(b) may be re-determined using equation (14). In someembodiments, one or more target image sequences corresponding to the atleast one second parameter may be generated. The one or more targetimage sequences may be used for the evaluation of the physiology(functionality) and/or anatomy (structure) of the ROI.

It should be noted that the above description regarding the process 600is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations or modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. In some embodiments, the target image sequences generatedbased on the parameters determined in the process 600 may be adequatefor diagnosis use and the process 700 may be omitted.

FIG. 8 is a flowchart illustrating an exemplary process for generatingat least one target image sequence corresponding to at least one seconddynamic parameter using an indirect reconstruction algorithm accordingto some embodiments of the present disclosure. At least a portion ofprocess 800 may be implemented on the computing device 200 asillustrated in FIG. 2 or the mobile device 300 as illustrated in FIG. 3.In some embodiments, one or more operations of the process 800 may beimplemented in the imaging system 100 as illustrated in FIG. 1. In someembodiments, one or more operations in the process 800 may be stored inthe storage device 150 and/or the storage 220 (e.g., a ROM, a RAM, etc.)as the form of instructions, and invoked and/or executed by theprocessing device 140, or the processor 210 of the processing device140. In some embodiments, the instructions may be transmitted to one ormore components of one or more components of the system 100 in the formof electronic current or electrical signals.

In 802, the processing device 140 (e.g., the target image sequencegeneration module 440) may determine an estimation function fordetermining at least one second dynamic parameter based on a projectionmodel, a second kinetic model, and the scan data. The projection modelmay be presented using the following equation (15):

Y(t)=AHC(t)+S(t)+R(t),  (15)

where Y(t) denotes the scan data; H denotes a projection matrix; Adenotes an attenuation effect; S(t) denotes a dynamic scattering effect;R(t) denotes random events. The estimation function may be determinedaccording to an iterative algorithm, such as an MLE algorithm, an OSEMalgorithm, a MAP algorithm, a WLS algorithm. Merely by way of example,the second kinetic model may be a two-tissue compartment model. Theestimation function may be presented as the following equation (16)using the MLE algorithm:

L(y,λ)=Σ_(i,t)−Σ_(j) AH _(i,j) C _(t)(λ_(j))+y _(i,t) log(Σ_(j) H _(i,j)C _(t)(λ_(j)))−log(y _(i,t)!),  (16)

where j denotes the pixels; and λ denotes a variable corresponding toone of K₁, k₂, k₃, k₄, and v_(b). t, the time that has lapsed since thetracer or agent administration, may be considered as a discretevariable.

In 804, the processing device 140 (e.g., the target image sequencegeneration module 440) may determine an iterative equation fordetermining the at least one second dynamic parameter based on theestimation function. In some embodiments, the impact of λ on theestimation function may be presented by the derivative as follow:

$\begin{matrix}{{\frac{\partial{L\left( {y,\lambda} \right)}}{\partial\lambda} = {\Sigma_{t}\frac{\partial{L\left( {y,\lambda} \right)}}{\partial C_{t}}\frac{\partial C_{t}}{\partial\lambda}}},} & (17)\end{matrix}$

where when

${\frac{\partial C_{t}}{\partial\lambda} > 0},$

the iterative equation for determining λ may be obtained in a mannersimilar to a traditional maximum likelihood expectation maximization(MLEM) algorithm. For example, the iterative equation may be presentedusing the following equation (18):

$\begin{matrix}{{\lambda_{j}^{n + 1} = {\frac{\lambda_{j}^{n}}{\Sigma_{i,t}H_{i,j}\frac{\partial C_{t}}{\partial\lambda}}\Sigma_{it}\frac{\frac{\partial C_{t}}{\partial\lambda}y_{i,t}H_{i,j}}{{\Sigma_{k}H_{i,k}{C_{t}\left( \lambda_{k} \right)}} + S_{t} + R_{t}}}}.} & (18)\end{matrix}$

Equation (18) may be used for estimating K₁, k₃, and v_(b). In someembodiments, the values of K₁, k₂, and v_(b) may be determined asdescribed in operation 606.

In some embodiments, when

${\frac{\partial C_{t}}{\partial\lambda} < 0},$

the iterative equation may be presented using the steepest descentalgorithm, for example, by the following equation (19):

$\begin{matrix}{{\lambda_{j}^{n + 1} = {\lambda_{j}^{n} + {s\frac{\partial{L\left( {y,\lambda} \right)}}{\partial\lambda_{j}}}}},} & (19)\end{matrix}$

where s denotes the step size for iterations, and s>0. The impact of λon the estimation function may be presented by the following equation(20):

$\begin{matrix}{{\frac{\partial{L\left( {y,\lambda} \right)}}{\partial\lambda_{j}} = {\sum_{t}{{- \frac{\partial C_{t}}{\partial\lambda}}\left( {{\sum_{i}H_{i,j}} - {y_{i,t}\frac{H_{i,j}}{{\Sigma_{k}H_{i,k}{C_{t}\left( \lambda_{k} \right)}} + S_{t} + R_{t}}}} \right)}}}.} & (20)\end{matrix}$

If the estimated parameter λ is always positive, positive constraint canbe added by transforming the additive update equation to amultiplicative update equation. It can be achieved by choosing theappropriate step size s. In some embodiments, s may be presented usingthe following equation (21):

$\begin{matrix}{{s = \frac{\lambda_{j}^{n}}{\Sigma_{t} - {\frac{\partial C_{t}}{\partial\lambda}\Sigma_{i}\frac{y_{i}H_{i,j}}{{\Sigma_{k}\Sigma_{k}H_{i,k}{C_{t}\left( \lambda_{k} \right)}} + S_{t} + R_{t}}}}}.} & (21)\end{matrix}$

In some embodiments, the iterative equation may be presented using thefollowing equation (22):

$\begin{matrix}{\lambda_{j}^{n + 1} = {{\lambda_{j}^{n} + {s\frac{\partial{L\left( {y,\lambda} \right)}}{\partial\lambda_{j}}}} = {{\lambda_{j}^{n} - {\frac{\lambda_{j}^{n}}{\Sigma_{t} - {\frac{\partial C_{t}}{\partial\lambda}\Sigma_{i}\frac{y_{i}H_{i,j}}{{\Sigma_{k}\Sigma_{k}H_{i,k}{C_{t}\left( \lambda_{k} \right)}} + S_{t} + R_{t}}}}{\sum\limits_{t}{{- \frac{\partial C_{t}}{\partial\lambda}}\left( {{\sum\limits_{i}H_{i,j}} - {y_{i,t}\frac{H_{i,j}}{{\Sigma_{k}H_{i,k}{C_{t}\left( \lambda_{k} \right)}} + S_{t} + R_{t}}}} \right)}}}} = \frac{\lambda_{j}^{n}{\sum_{i,t}{H_{i,j}\frac{\partial C_{t}}{\partial\lambda}}}}{\Sigma_{t}\frac{\partial C_{t}}{\partial\lambda}\Sigma_{i}\frac{y_{i}H_{i,j}}{{\Sigma_{k}\Sigma_{k}H_{i,k}{C_{t}\left( \lambda_{k} \right)}} + S_{t} + R_{t}}}}}} & (22)\end{matrix}$

Equation (22) may be used for estimating k₂ and k₄.

In 806, the processing device 140 (e.g., the target image sequencegeneration module 440) may generate at least one target image sequencecorresponding to the at least one second dynamic parameter by performingan iterative operation based on the iterative equation. In someembodiments, one or more target image sequences corresponding to the atleast one second parameter may be generated. The one or more targetimage sequences may be used for the evaluation of the physiology(functionality) and/or anatomy (structure) of the ROI.

In some embodiments, other indirect or direct reconstruction algorithmsmay also be adopted to generate the at least one target image sequencecorresponding to the at least one second dynamic parameter, which arenot limited by the present disclosure. For instance, a nestedreconstruction algorithm may be adopted. The processing device 140 maydetermine, for each of a plurality of pixels in a target image sequenceand according to an alternative and iterative process, the outputfunction and the at least one second dynamic parameter. For example, theprocessing device 140 may determine a first iterative equation forestimating the output function using the projection model (e.g.,presented by equation (11)). The processing device 140 may determine asecond iterative equation for estimating the at least one second dynamicparameter based on the output function using the second kinetic model.The processing device 140 may determine an initial value for the outputfunction. In some embodiments, the initial value for the output functionmay be determined using an image reconstruction algorithm for generatingan SUV image based on the scan data. In the alternative and iterativeprocess, the processing device 140 may update the at least one seconddynamic parameter based on the second iterative equation and the currentvalue of the output function; and the processing device 140 may updatethe output function based on the current value(s) of the at least onesecond dynamic parameter. The alternative and iterative process may beperformed repeatedly until convergence is reached. The processing device140 may further generate at least one target image sequencecorresponding to the at least one second dynamic parameter based on thevalue(s) of the at least one second dynamic parameter in the lastiteration.

It should be noted that the above description regarding the process 600is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations or modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. In some embodiments, the target image sequences generatedbased on the parameters determined in the process 600 may be adequatefor diagnosis use and the process 800 may be omitted.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “module,” “unit,” “component,” “device,” or “system.”Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readable mediahaving computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby, andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose and that the appended claimsare not limited to the disclosed embodiments, but, on the contrary, areintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the disclosed embodiments. For example,although the implementation of various components described above may beembodied in a hardware device, it may also be implemented as a softwareonly solution, e.g., an installation on an existing server or mobiledevice.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various embodiments. This method ofdisclosure, however, is not to be interpreted as reflecting an intentionthat the claimed subject matter requires more features than areexpressly recited in each claim. Rather, claim subject matter lies inless than all features of a single foregoing disclosed embodiment.

What is claimed is:
 1. A system for image reconstruction, comprising: at least one non-transitory storage medium including a set of instructions; and at least one processor in communication with the at least one non-transitory storage medium, wherein when executing the set of instructions, the at least one processor is configured to cause the system to perform operations including: obtaining a first image sequence of a subject, the first image sequence including one or more first images generated based on a first portion of scan data of a scan of the subject; obtaining an initial input function that relates to a concentration of an agent in blood vessels of the subject with respect to time, wherein the agent is administered to the subject before the scan; for each of a plurality of pixels in the one or more first images, determining at least one correction parameter associated with the pixel; and determining, based on the initial input function and the at least one correction parameter, a target input function associated with the pixel; and generating one or more target image sequences based at least in part on a plurality of target input functions associated with the plurality of pixels in the one or more first images, wherein the one or more target image sequences relate to one or more dynamic parameters associated with the subject, respectively.
 2. The system of claim 1, wherein the at least one correction parameter includes at least one of a first parameter associated with a dispersion effect caused by blood circulation, or a second parameter associated with a time delay effect caused by blood circulation.
 3. The system of claim 1, wherein the agent includes a radioactive tracer.
 4. The system of claim 1, wherein the one or more dynamic parameters include a set of first dynamic parameters, and to determine the at least one correction parameter associated with the pixel, the at least one processor is configured to cause the system to perform operations including: determining, based on the one or more first images in the first image sequence, an output function that indicates a concentration of the agent in at least a part of a tissue of the subject; determining, using a first kinetic model, a relationship among the set of first dynamic parameters associated with the subject, the initial input function, the at least one correction parameter, and the output function; and determining the at least one correction parameter based on the relationship, the initial input function, and the output function.
 5. The system of claim 4, wherein the first kinetic model is a one-tissue compartment model.
 6. The system of claim 4, wherein to determine the at least one correction parameter associated with the initial input function, the at least one processor is further configured to cause the system to perform operations including: determining the at least one correction parameter based further on a preset condition associated with one or more physiological properties of the subject.
 7. The system of claim 4, wherein to generate the one or more target image sequences, the at least one processor is further configured to cause the system to perform operations including: generating, based on the relationship among the set of first dynamic parameters, at least one of the one or more target image sequences corresponding to at least one of the set of first dynamic parameters.
 8. The system of claim 1, wherein the one or more dynamic parameters include a set of second dynamic parameters, and to generate the one or more target image sequences, the at least one processor is configured to cause the system to perform operations including: obtaining a second image sequence generated based on a second portion of the scan data, the second image sequence including one or more second images; and generating at least one of the one or more target image sequences corresponding to at least one second dynamic parameter based on a second kinetic model, the second image sequence, and the plurality of target input functions associated with the plurality of pixels in the one or more first images.
 9. The system of claim 8, wherein the second portion of the scan data at least partially overlaps the first portion of the scan data.
 10. The system of claim 8, wherein the second portion of the scan data include the first portion of the scan data.
 11. The system of claim 1, wherein to generate the one or more target image sequences, the at least one processor is configured to cause the system to perform operations including: generating at least one of the one or more target image sequences corresponding to at least one second dynamic parameter by performing an iterative operation based on a projection model, a second kinetic model, and the scan data.
 12. The system of claim 11, wherein the iterative operation includes a maximum likelihood estimation operation.
 13. A method for image reconstruction, implemented on a computing device having at least one processor and at least one non-transitory storage medium, the method comprising: obtaining a first image sequence of a subject, the first image sequence including one or more first images generated based on a first portion of scan data of a scan of the subject; obtaining an initial input function that relates to a concentration of an agent in blood vessels of the subject with respect to time, wherein the agent is administered to the subject before the scan; for each of a plurality of pixels in the one or more first images, determining at least one correction parameter associated with the pixel; and determining, based on the initial input function and the at least one correction parameter, a target input function associated with the pixel; and generating one or more target image sequences based at least in part on a plurality of target input functions associated with the plurality of pixels in the one or more first images, wherein the one or more target image sequences relate to one or more dynamic parameters associated with the subject, respectively.
 14. The method of claim 13, wherein the at least one correction parameter includes at least one of a first parameter associated with a dispersion effect caused by blood circulation, or a second parameter associated with a time delay effect caused by blood circulation.
 15. The method of claim 13, wherein the agent includes a radioactive tracer.
 16. The method of claim 13, wherein the one or more dynamic parameters include a set of first dynamic parameters, and the determining at least one correction parameter associated with the pixel includes: determining, based on the one or more first images in the first image sequence, an output function that indicates a concentration of the agent in at least a part of a tissue of the subject; determining, using a first kinetic model, a relationship among the set of first dynamic parameters associated with the subject, the initial input function, the at least one correction parameter, and the output function; and determining the at least one correction parameter based on the relationship, the initial input function, and the output function.
 17. The method of claim 16, wherein the generating one or more target image sequences based at least in part on a plurality of target input functions associated with the plurality of pixels in the one or more first images includes: generating, based on the relationship among the set of first dynamic parameters, at least one of the one or more target image sequences corresponding to at least one of the set of first dynamic parameters.
 18. The method of claim 13, wherein the one or more dynamic parameters include a set of second dynamic parameters, and the generating one or more target image sequences includes: obtaining a second image sequence generated based on a second portion of the scan data, the second image sequence including one or more second images; and generating at least one of the one or more target image sequences corresponding to at least one second dynamic parameter based on a second kinetic model, the second image sequence, and the plurality of target input functions associated with the plurality of pixels in the one or more first images.
 19. The method of claim 13, wherein the generating one or more target image sequences based at least in part on a plurality of target input functions associated with the plurality of pixels in the one or more first images includes: generating at least one of the one or more target image sequences corresponding to at least one second dynamic parameter by performing an iterative operation based on a projection model, a second kinetic model, and the scan data.
 20. A non-transitory computer readable medium, comprising at least one set of instructions for image reconstruction, wherein when executed by at least one processor of a computing device, the at least one set of instructions direct the at least one processor to perform operations including: obtaining a first image sequence of a subject, the first image sequence including one or more first images generated based on a first portion of scan data of a scan of the subject; obtaining an initial input function that relates to a concentration of an agent in blood vessels of the subject with respect to time, wherein the agent is administered to the subject before the scan; for each of a plurality of pixels in the one or more first images, determining at least one correction parameter associated with the pixel; and determining, based on the initial input function and the at least one correction parameter, a target input function associated with the pixel; and generating one or more target image sequences based at least in part on a plurality of target input functions associated with the plurality of pixels in the one or more first images, wherein the one or more target image sequences relate to one or more dynamic parameters associated with the subject, respectively. 